Overview

Dataset statistics

Number of variables16
Number of observations45408
Missing cells90816
Missing cells (%)12.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.5 MiB
Average record size in memory128.0 B

Variable types

NUM12
UNSUPPORTED2
CAT1
DATE1

Warnings

client_id has constant value "45408" Constant
total_tx_data has 45408 (100.0%) missing values Missing
total_rx_data has 45408 (100.0%) missing values Missing
stats_timestamp has unique values Unique
total_tx_data is an unsupported type, check if it needs cleaning or further analysis Unsupported
total_rx_data is an unsupported type, check if it needs cleaning or further analysis Unsupported
total_attached_user has 688 (1.5%) zeros Zeros
total_rejected_user has 7523 (16.6%) zeros Zeros
peak_download_speed has 688 (1.5%) zeros Zeros
enodeb_shutdown_count has 4380 (9.6%) zeros Zeros
handover_failure_count has 4428 (9.8%) zeros Zeros
bearer_active_user_count has 4425 (9.7%) zeros Zeros
bearer_rejected_user_count has 4547 (10.0%) zeros Zeros
total_users has 4499 (9.9%) zeros Zeros
total_dropped_packets has 688 (1.5%) zeros Zeros
enodeb_connected_count has 4468 (9.8%) zeros Zeros
enodeb_connection_status has 4421 (9.7%) zeros Zeros

Reproduction

Analysis started2022-09-21 17:21:39.355983
Analysis finished2022-09-21 17:21:53.721689
Duration14.37 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

client_id
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size354.8 KiB
BETBEL01GYN001
45408 
ValueCountFrequency (%) 
BETBEL01GYN00145408100.0%
 
2022-09-21T10:21:53.765279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-09-21T10:21:53.806797image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:53.844146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length14
Median length14
Mean length14
Min length14

stats_timestamp
Date

UNIQUE

Distinct45408
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size354.8 KiB
Minimum2022-07-14 10:56:50
Maximum2022-09-21 10:19:56
2022-09-21T10:21:53.912676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:53.992337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

total_attached_user
Real number (ℝ≥0)

ZEROS

Distinct77
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.24696089
Minimum0
Maximum80
Zeros688
Zeros (%)1.5%
Memory size354.8 KiB
2022-09-21T10:21:54.082387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q120
median31
Q343
95-th percentile61
Maximum80
Range80
Interquartile range (IQR)23

Descriptive statistics

Standard deviation15.46019435
Coefficient of variation (CV)0.4794310509
Kurtosis0.2494035011
Mean32.24696089
Median Absolute Deviation (MAD)11
Skewness0.5482659839
Sum1464270
Variance239.0176092
MonotocityNot monotonic
2022-09-21T10:21:54.161580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5011222.5%
 
1910622.3%
 
2510452.3%
 
1610372.3%
 
3610302.3%
 
1010292.3%
 
2810292.3%
 
2010282.3%
 
3110282.3%
 
2410252.3%
 
Other values (67)3497377.0%
 
ValueCountFrequency (%) 
06881.5%
 
51< 0.1%
 
66< 0.1%
 
75< 0.1%
 
86< 0.1%
 
ValueCountFrequency (%) 
801200.3%
 
791220.3%
 
781150.3%
 
771210.3%
 
761170.3%
 

total_rejected_user
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.785192037
Minimum0
Maximum8
Zeros7523
Zeros (%)16.6%
Memory size354.8 KiB
2022-09-21T10:21:54.231668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile6
Maximum8
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.007335432
Coefficient of variation (CV)0.7207170657
Kurtosis-0.5906881302
Mean2.785192037
Median Absolute Deviation (MAD)2
Skewness0.3201720392
Sum126470
Variance4.029395536
MonotocityNot monotonic
2022-09-21T10:21:54.285978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
5782517.2%
 
0752316.6%
 
4688215.2%
 
1686215.1%
 
3686015.1%
 
2676614.9%
 
89122.0%
 
68992.0%
 
78791.9%
 
ValueCountFrequency (%) 
0752316.6%
 
1686215.1%
 
2676614.9%
 
3686015.1%
 
4688215.2%
 
ValueCountFrequency (%) 
89122.0%
 
78791.9%
 
68992.0%
 
5782517.2%
 
4688215.2%
 

peak_upload_speed
Real number (ℝ≥0)

Distinct13718
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46387.62971
Minimum20054
Maximum79991
Zeros0
Zeros (%)0.0%
Memory size354.8 KiB
2022-09-21T10:21:54.361760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum20054
5-th percentile40419.35
Q142614.75
median45371.5
Q348109
95-th percentile60985.2
Maximum79991
Range59937
Interquartile range (IQR)5494.25

Descriptive statistics

Standard deviation6752.364731
Coefficient of variation (CV)0.1455639094
Kurtosis8.327856389
Mean46387.62971
Median Absolute Deviation (MAD)2747.5
Skewness2.320459002
Sum2106369490
Variance45594429.46
MonotocityNot monotonic
2022-09-21T10:21:54.436594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
28251400.1%
 
4197716< 0.1%
 
4617116< 0.1%
 
4768414< 0.1%
 
4002714< 0.1%
 
4322613< 0.1%
 
4132213< 0.1%
 
4193113< 0.1%
 
4641812< 0.1%
 
4699512< 0.1%
 
Other values (13708)4524599.6%
 
ValueCountFrequency (%) 
200541< 0.1%
 
201141< 0.1%
 
201471< 0.1%
 
201811< 0.1%
 
201981< 0.1%
 
ValueCountFrequency (%) 
799911< 0.1%
 
799862< 0.1%
 
799811< 0.1%
 
799751< 0.1%
 
799731< 0.1%
 

peak_download_speed
Real number (ℝ≥0)

ZEROS

Distinct12997
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15645.82063
Minimum0
Maximum31996
Zeros688
Zeros (%)1.5%
Memory size354.8 KiB
2022-09-21T10:21:54.520455image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10371
Q112609
median15370
Q318112
95-th percentile24370.65
Maximum31996
Range31996
Interquartile range (IQR)5503

Descriptive statistics

Standard deviation4597.249955
Coefficient of variation (CV)0.2938324594
Kurtosis2.825411458
Mean15645.82063
Median Absolute Deviation (MAD)2753
Skewness0.4669793372
Sum710445423
Variance21134707.15
MonotocityNot monotonic
2022-09-21T10:21:54.595228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
06881.5%
 
1605113< 0.1%
 
1003113< 0.1%
 
1306513< 0.1%
 
1821713< 0.1%
 
1965813< 0.1%
 
1833312< 0.1%
 
1872612< 0.1%
 
1996312< 0.1%
 
1162012< 0.1%
 
Other values (12987)4460798.2%
 
ValueCountFrequency (%) 
06881.5%
 
58001< 0.1%
 
61931< 0.1%
 
62491< 0.1%
 
64441< 0.1%
 
ValueCountFrequency (%) 
319961< 0.1%
 
319932< 0.1%
 
319801< 0.1%
 
319791< 0.1%
 
319681< 0.1%
 

enodeb_shutdown_count
Real number (ℝ≥0)

ZEROS

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.573577343
Minimum0
Maximum16
Zeros4380
Zeros (%)9.6%
Memory size354.8 KiB
2022-09-21T10:21:54.662576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile12
Maximum16
Range16
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.808784855
Coefficient of variation (CV)0.683364493
Kurtosis-0.4357941262
Mean5.573577343
Median Absolute Deviation (MAD)3
Skewness0.3882055544
Sum253085
Variance14.50684207
MonotocityNot monotonic
2022-09-21T10:21:54.717496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%) 
043809.6%
 
1043429.6%
 
938018.4%
 
337978.4%
 
237888.3%
 
637818.3%
 
537558.3%
 
737428.2%
 
136838.1%
 
836368.0%
 
Other values (7)670314.8%
 
ValueCountFrequency (%) 
043809.6%
 
136838.1%
 
237888.3%
 
337978.4%
 
436288.0%
 
ValueCountFrequency (%) 
164931.1%
 
155071.1%
 
145501.2%
 
135471.2%
 
124891.1%
 

handover_failure_count
Real number (ℝ≥0)

ZEROS

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.557500881
Minimum0
Maximum16
Zeros4428
Zeros (%)9.8%
Memory size354.8 KiB
2022-09-21T10:21:54.779496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile12
Maximum16
Range16
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.80768518
Coefficient of variation (CV)0.6851434236
Kurtosis-0.4218168322
Mean5.557500881
Median Absolute Deviation (MAD)3
Skewness0.3909068823
Sum252355
Variance14.49846643
MonotocityNot monotonic
2022-09-21T10:21:54.834461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%) 
044289.8%
 
1042399.3%
 
838158.4%
 
637788.3%
 
337728.3%
 
937598.3%
 
137518.3%
 
237468.2%
 
737158.2%
 
536838.1%
 
Other values (7)672214.8%
 
ValueCountFrequency (%) 
044289.8%
 
137518.3%
 
237468.2%
 
337728.3%
 
436608.1%
 
ValueCountFrequency (%) 
165111.1%
 
155251.2%
 
145141.1%
 
134771.1%
 
124941.1%
 

bearer_active_user_count
Real number (ℝ≥0)

ZEROS

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.567168781
Minimum0
Maximum16
Zeros4425
Zeros (%)9.7%
Memory size354.8 KiB
2022-09-21T10:21:54.896424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile12
Maximum16
Range16
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.804269072
Coefficient of variation (CV)0.6833399923
Kurtosis-0.4138335223
Mean5.567168781
Median Absolute Deviation (MAD)3
Skewness0.3887477199
Sum252794
Variance14.47246317
MonotocityNot monotonic
2022-09-21T10:21:54.950595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%) 
044259.7%
 
1041439.1%
 
638338.4%
 
938228.4%
 
738168.4%
 
837688.3%
 
137488.3%
 
337418.2%
 
437258.2%
 
536618.1%
 
Other values (7)672614.8%
 
ValueCountFrequency (%) 
044259.7%
 
137488.3%
 
236418.0%
 
337418.2%
 
437258.2%
 
ValueCountFrequency (%) 
165111.1%
 
155101.1%
 
145351.2%
 
135071.1%
 
125051.1%
 

bearer_rejected_user_count
Real number (ℝ≥0)

ZEROS

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.559923362
Minimum0
Maximum16
Zeros4547
Zeros (%)10.0%
Memory size354.8 KiB
2022-09-21T10:21:55.012473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile12
Maximum16
Range16
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.819797757
Coefficient of variation (CV)0.6870234549
Kurtosis-0.4328376346
Mean5.559923362
Median Absolute Deviation (MAD)3
Skewness0.3899723522
Sum252465
Variance14.5908549
MonotocityNot monotonic
2022-09-21T10:21:55.070657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%) 
0454710.0%
 
1042679.4%
 
538678.5%
 
937628.3%
 
837408.2%
 
237318.2%
 
437168.2%
 
637098.2%
 
136768.1%
 
736498.0%
 
Other values (7)674414.9%
 
ValueCountFrequency (%) 
0454710.0%
 
136768.1%
 
237318.2%
 
336358.0%
 
437168.2%
 
ValueCountFrequency (%) 
164991.1%
 
155241.2%
 
145421.2%
 
135171.1%
 
125281.2%
 

total_users
Real number (ℝ≥0)

ZEROS

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.54005902
Minimum0
Maximum16
Zeros4499
Zeros (%)9.9%
Memory size354.8 KiB
2022-09-21T10:21:55.132545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile12
Maximum16
Range16
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.815846878
Coefficient of variation (CV)0.6887736871
Kurtosis-0.4115652841
Mean5.54005902
Median Absolute Deviation (MAD)3
Skewness0.4032462839
Sum251563
Variance14.5606874
MonotocityNot monotonic
2022-09-21T10:21:55.186626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%) 
044999.9%
 
1042319.3%
 
737898.3%
 
237648.3%
 
637628.3%
 
137608.3%
 
537528.3%
 
837198.2%
 
437198.2%
 
337028.2%
 
Other values (7)671114.8%
 
ValueCountFrequency (%) 
044999.9%
 
137608.3%
 
237648.3%
 
337028.2%
 
437198.2%
 
ValueCountFrequency (%) 
165151.1%
 
155191.1%
 
145351.2%
 
135101.1%
 
124891.1%
 

total_dropped_packets
Real number (ℝ≥0)

ZEROS

Distinct77
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.38960095
Minimum0
Maximum80
Zeros688
Zeros (%)1.5%
Memory size354.8 KiB
2022-09-21T10:21:55.267050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q120
median32
Q343
95-th percentile61
Maximum80
Range80
Interquartile range (IQR)23

Descriptive statistics

Standard deviation15.44282706
Coefficient of variation (CV)0.4767834924
Kurtosis0.2334887546
Mean32.38960095
Median Absolute Deviation (MAD)11
Skewness0.5299801037
Sum1470747
Variance238.4809076
MonotocityNot monotonic
2022-09-21T10:21:55.349275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5011142.5%
 
4210632.3%
 
4010582.3%
 
4510472.3%
 
2610462.3%
 
1010432.3%
 
2210392.3%
 
4410382.3%
 
3510372.3%
 
4910262.3%
 
Other values (67)3489776.9%
 
ValueCountFrequency (%) 
06881.5%
 
51< 0.1%
 
63< 0.1%
 
76< 0.1%
 
86< 0.1%
 
ValueCountFrequency (%) 
801090.2%
 
791120.2%
 
781240.3%
 
771090.2%
 
761160.3%
 

enodeb_connected_count
Real number (ℝ≥0)

ZEROS

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.548163319
Minimum0
Maximum16
Zeros4468
Zeros (%)9.8%
Memory size354.8 KiB
2022-09-21T10:21:55.422543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile12
Maximum16
Range16
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.818170728
Coefficient of variation (CV)0.6881864336
Kurtosis-0.4126377066
Mean5.548163319
Median Absolute Deviation (MAD)3
Skewness0.4039062889
Sum251931
Variance14.57842771
MonotocityNot monotonic
2022-09-21T10:21:55.481255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%) 
044689.8%
 
1043569.6%
 
638138.4%
 
537888.3%
 
437718.3%
 
137668.3%
 
337278.2%
 
237178.2%
 
936938.1%
 
836218.0%
 
Other values (7)668814.7%
 
ValueCountFrequency (%) 
044689.8%
 
137668.3%
 
237178.2%
 
337278.2%
 
437718.3%
 
ValueCountFrequency (%) 
165191.1%
 
155501.2%
 
145121.1%
 
134671.0%
 
125061.1%
 

enodeb_connection_status
Real number (ℝ≥0)

ZEROS

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.563028541
Minimum0
Maximum16
Zeros4421
Zeros (%)9.7%
Memory size354.8 KiB
2022-09-21T10:21:55.542923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile12
Maximum16
Range16
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.812689964
Coefficient of variation (CV)0.685362287
Kurtosis-0.4321072525
Mean5.563028541
Median Absolute Deviation (MAD)3
Skewness0.3901191473
Sum252606
Variance14.53660476
MonotocityNot monotonic
2022-09-21T10:21:55.601834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%) 
044219.7%
 
1042819.4%
 
238398.5%
 
937638.3%
 
637628.3%
 
537598.3%
 
837428.2%
 
137218.2%
 
336958.1%
 
736818.1%
 
Other values (7)674414.9%
 
ValueCountFrequency (%) 
044219.7%
 
137218.2%
 
238398.5%
 
336958.1%
 
436418.0%
 
ValueCountFrequency (%) 
165161.1%
 
155061.1%
 
145141.1%
 
135151.1%
 
125021.1%
 

total_tx_data
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing45408
Missing (%)100.0%
Memory size354.9 KiB

total_rx_data
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing45408
Missing (%)100.0%
Memory size354.9 KiB

Interactions

2022-09-21T10:21:40.356435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:40.440595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:40.536673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:40.635977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:40.734080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:40.830054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:40.926215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:41.012087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:41.099149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:41.189624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:41.271811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:41.354066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:41.435578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:41.514597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:41.593606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:41.672025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:41.751792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:41.837281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:41.917379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:41.997979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:42.078800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:42.155602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:42.243649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:42.323725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:42.403976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:42.479084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:42.555871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:42.632160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:42.709556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:42.788723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:43.159638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:43.246979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:43.335912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:43.414285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:43.490671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:43.572748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:43.653510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:43.743431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:43.821339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:43.897574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:43.976901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:44.053423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:44.133825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:44.214640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:44.297040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:44.379459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:44.460764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:44.540247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:44.621423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:44.707550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:44.787353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:44.869384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:44.961606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:45.042507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:45.133625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:45.217690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:45.309252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:45.398973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:45.481706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:45.566473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:45.647205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:45.730112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:45.818481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:45.904580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:45.986974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:46.071058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:46.152823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:46.236763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:46.318242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:46.400341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:46.494859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:46.578893image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:46.661673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:46.749483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:47.016477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:47.100774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:47.181260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:47.265108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:47.349474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:47.434822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:47.523807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:47.605642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:47.688329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:47.788737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:47.881453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:47.969801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:48.050431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:48.138380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:48.225010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:48.308419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:48.390591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:48.474885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:48.577227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:48.659176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:48.750115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:48.839258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:48.928805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:49.012702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:49.095575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:49.177444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:49.262585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:49.342450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:49.428740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:49.510840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:49.601190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:49.686534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:49.778836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:49.863610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:49.949056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:50.031728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:50.116048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:50.197668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:50.281613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:50.362902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:50.449225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:50.531550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:50.620109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:50.699097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:50.784825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:50.868677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:50.956647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:51.037116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:51.122748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:51.210019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:51.290293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:51.370055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:51.452675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:51.746019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:51.825983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:51.905733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:51.988735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:52.071717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:52.152471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:52.230075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:52.310323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:52.390818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:52.471213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:52.555422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:52.636059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:52.716590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:52.813277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:52.893087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:52.981193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:53.070822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-09-21T10:21:55.668517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-21T10:21:55.796140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-21T10:21:55.917418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-21T10:21:56.061107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-21T10:21:53.228429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:53.448971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-21T10:21:53.623557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Sample

First rows

client_idstats_timestamptotal_attached_usertotal_rejected_userpeak_upload_speedpeak_download_speedenodeb_shutdown_counthandover_failure_countbearer_active_user_countbearer_rejected_user_counttotal_userstotal_dropped_packetsenodeb_connected_countenodeb_connection_statustotal_tx_datatotal_rx_data
0BETBEL01GYN0012022-07-14 10:56:500028251000000000.0NoneNone
1BETBEL01GYN0012022-07-14 11:05:550028251000000000.0NoneNone
2BETBEL01GYN0012022-07-14 11:07:040028251000000000.0NoneNone
3BETBEL01GYN0012022-07-14 11:08:030028251000000000.0NoneNone
4BETBEL01GYN0012022-07-14 11:08:050028251000000000.0NoneNone
5BETBEL01GYN0012022-07-14 11:08:070028251000000000.0NoneNone
6BETBEL01GYN0012022-07-14 11:08:090028251000000000.0NoneNone
7BETBEL01GYN0012022-07-14 11:08:110028251000000000.0NoneNone
8BETBEL01GYN0012022-07-14 11:08:130028251000000000.0NoneNone
9BETBEL01GYN0012022-07-14 11:08:150028251000000000.0NoneNone

Last rows

client_idstats_timestamptotal_attached_usertotal_rejected_userpeak_upload_speedpeak_download_speedenodeb_shutdown_counthandover_failure_countbearer_active_user_countbearer_rejected_user_counttotal_userstotal_dropped_packetsenodeb_connected_countenodeb_connection_statustotal_tx_datatotal_rx_data
45398BETBEL01GYN0012022-09-21 10:01:552714676215396101118431010.0NoneNone
45399BETBEL01GYN0012022-09-21 10:03:561404901816710640953106.0NoneNone
45400BETBEL01GYN0012022-09-21 10:05:561004934413201230972912.0NoneNone
45401BETBEL01GYN0012022-09-21 10:07:563744142518885268604529.0NoneNone
45402BETBEL01GYN0012022-09-21 10:09:562724635913725101081093668.0NoneNone
45403BETBEL01GYN0012022-09-21 10:11:5649144147111993310493057.0NoneNone
45404BETBEL01GYN0012022-09-21 10:13:5613247262188251087612279.0NoneNone
45405BETBEL01GYN0012022-09-21 10:15:564934787611605239401351.0NoneNone
45406BETBEL01GYN0012022-09-21 10:17:562724277916834876221625.0NoneNone
45407BETBEL01GYN0012022-09-21 10:19:563824499011970785853956.0NoneNone